Transcript Presentation Title (26pt, Arial Bold)

When to Use CDISC Standards A practical experience gained from a
US submission with Pharmacokinetic data
BASAS, July 5 2011
Kristie Kooken, Scott Bahlavooni, Amy Klopman
Objectives for today’s talk
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To better understand CDISC guidance for
Pharmacokinetic (PK) data
To hear first-hand experience using these guidance
on a complex filing
To learn about possible challenges with CDISC
models for PK data and solutions we implemented
STATISTICAL PROGRAMMING AND ANALYSIS
Project Background
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New Molecular Entity with large PK piece – filing for New Drug Application
(NDA)
Small Molecule (taken orally)
PK piece is comprised of two types of studies:
Healthy Volunteers (HV)
Patient Studies
Retrospectively mapped to SDTM structure
PK piece includes 7 studies
4 are fully-outsourced
3 are in-house by Genentech
PK & PD analytes for each study
PK analyses cover:
PK characteristics of the drug
Food effects - label enabling
Exposure-response analyses for efficacy and safety
QTc – label enabling
Population PK modeling to determine covariates that
could potentially alter dose-concentration relationships
STATISTICAL PROGRAMMING AND ANALYSIS
Our Goal
Is To
Develop
Support
Datasets
Data and Data Handling
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PK data (any type of assay data) is complex
Time by drug concentrations is unit of analysis
Concentration data receipt is not aligned with other clinical data
PK analysis (i.e. calculating PK parameters), typically occurs
late in the clinical trial
At Genentech, a specialty programming group oversees all PK
deliverables from Statistical Programming and Analysis
Allows for a deeper understanding of this data type, the purpose
of various analyses and greater contribution to analyses
STATISTICAL PROGRAMMING AND ANALYSIS
PK Data: time vs. concentration plot
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100
Serum anti-IL13 (µg/mL)
1 mg/kg SC
10
1
0.1
0.01
0
14
28
42
Time (day)
STATISTICAL PROGRAMMING AND ANALYSIS
56
70
84
98
CDISC Overview
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Mission Statement:
To develop and support global, platform-independent data standards that enable
information system interoperability to improve medical research and related areas of
healthcare
Study Data Tabulation Model (SDTM):
[To] define a standard structure for study data tabulations that are to be submitted
as part of a product application to a regulatory authority
Analysis Data Model (ADaM):
To provide a framework that enables analysis of the data, while at the same time
allowing reviewers and other recipients of the data to have a clear understanding of
the data’s lineage from collection to analysis to results
STATISTICAL PROGRAMMING AND ANALYSIS
CDISC Data Flow
STATISTICAL PROGRAMMING AND ANALYSIS
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SDTM PK Domains
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Pharmacokinetic Concentrations (PC)
Data collected about concentrations of analytes from blood samples
as function of time after dosing
One record per concentration or sample characteristic
Pharmacokinetic Parameters (PP)
Data describing the parameters of the time-concentration curve for
PC data, such as Tmax, HL, AUC, Cmax
One record per PK parameter per time-concentration profile
It is recognized the PP is a derived dataset and may be produced
from an analysis dataset
STATISTICAL PROGRAMMING AND ANALYSIS
SDTM PK Domain Ambiguity
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At DIA/FDA CDER/CBER Computational Science Annual
Meeting, Mar 2011 - Helena Sviglin, CDER, stated the FDA is
working to better understand PK domains
Should laboratory/PD assessments be included in PC?
Examples: Albumin, urine volume, time-point based glucose
How to relate records in PC to PP?
PPRFTDTC
Analysis metadata
RELREC
How to implement controlled terminology?
PK CT released in 2010
STATISTICAL PROGRAMMING AND ANALYSIS
Practical Experience: SDTM & PK
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PC and PP domains mapped retrospectively for 4 of 7 studies
Pharmacologists consulted to align reported parameter names with CDISC
PK parameter controlled terminology
 Selecting the appropriate method to relate PC and PP
Consulted with internal and external subject matter experts including
SDTM experts, leading industry experts, Pharmacologists
Differing opinions for defining reference time point
Protocol defined collection schedule vs. analysis time points
Decision:
Protocol defined collection schedule is represented in PC timing
variables. PPRFTDTC is defined as the analysis reference date.
PPGRPID is defined to indicated the concentrations used to calculate
the parameter.
The specific linking between dosing datetime and PK concentration
datetime are described in the analysis metadata
STATISTICAL PROGRAMMING AND ANALYSIS
Considerations for SDTM & PK (1)
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Sponsor
Define and document a methodology for defining PK SDTM timing
variables and relating PC and PP domains
Document (e.g. a datastore) mapping reported parameter names to
CDISC PK terminology
Study
Prospectively map PC and PP domains
Obtain "test data" early in the clinical study life-cycle
Ensure time point reference dates are clearly defined and collected
Complicated derivations should not be required to assign time point
reference dates
Collaborate with the Pharmacologist to clearly document the
concentrations used to calculate each PK parameter
Confirm with the Pharmacologist what PK parameters are reported in
the Clinical Study Report(s)
STATISTICAL PROGRAMMING AND ANALYSIS
Considerations for SDTM & PK (2)
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CDISC
Reassess PK domains
Does the SDTM IG provide clear guidance to sponsors?
Do the SDTM IG examples sufficiently document various PK data
analyses?
Are the examples in the SDTM IG aligned with the controlled
terminology?
Can the relationship between the domains be clearly defined?
Solicit sponsor and (FDA) reviewer feedback
Do PK domains meet sponsor tabulation and analytical needs?
Do PK domains meet (FDA) review needs?
STATISTICAL PROGRAMMING AND ANALYSIS
CDISC Guidance for ADaM
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ADSL
The ADSL dataset contains one record per subject
Population flags, planned and actual treatment variables for each
period, demographic information, stratification and subgrouping
variables, etc.
ADSL and its related metadata are required in a CDISC-based
submission of data from a clinical trial even if no other analysis
datasets are submitted
Basic Data Structure (BDS)
Dataset with one or more records per subject, per analysis parameter,
per analysis time point
Specific features to map for PK data:
Central set of variables represent the data being analyzed
PARAM – Description of the value being analyzed
AVAL – Actual value being analyzed
Timing variables – ADT (date), ATM (time)
STATISTICAL PROGRAMMING AND ANALYSIS
ADaM BDS Ambiguity
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ADaM Implementation Guide states:
Though the BDS supports most statistical analyses, it does not support
all statistical analyses... it does not support simultaneous analysis of
multiple dependent (response/outcome) variables or a correlation
analysis across a range of response variables. The BDS was not
designed to support analysis of incidence of adverse events or other
occurrence data
This version of the implementation guide does not fully cover dose
escalation trials or integration of multiple studies.
Use ADaM for PK data where it fits…
How did we determine when and where to use BDS?
ADaM BDS works well
Concentration analysis dataset to support summary outputs
ADaMIG is not clear
Datasets used to support modeling or explore PKPD
relationships
STATISTICAL PROGRAMMING AND ANALYSIS
Practical Experience: ADaM & PK (1)
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Gathered advice from subject matter experts within & outside of Statistical
Programming and Analysis:
Met with Genentech Global Data Standards team
Developed basic mapping for ADaM domains
Clinical Pharmacology group met with Modeling & Simulation expert - Marc
Gastonguay (Metrum Research Group)
Determined analysis format for Pharmacometric Reviewer
Developed a systematic data plan for each analysis:
PART 1:
Clinical Study Reports – 6 studies
ADaM structured datasets to support TLG generation
ADPC – Pharmacokinetic concentrations supporting summary
outputs
Mapped assay data in support of TLG (inclusion of PD data
collected as Safety Labs)
ADPP – Pharmacokinetic parameters supporting summary outputs
STATISTICAL PROGRAMMING AND ANALYSIS
Practical Experience: ADaM & PK (2)
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PART 2:
QTc Analysis
Description: ECG and PK concentrations at matching timepoints
Purpose: QTc changes over drug concentration level could be potentially
dangerous (cardiotoxicity)
Significant increase from baseline is a clinical concern
Design: Analysis dataset structured to best fit analysis
ADaM-like dataset, all ADSL variables were present
PKPD analyses
Description: Exposure-response analyses with Efficacy / Safety
Purpose: Explore the relationship between drug exposure and
pharmacodynamic measures
Design: Structured for NONMEM or S-Plus software
Population PK (5 studies)
Description: Drug concentration data formatted for statistical modeling
Purpose: Estimate the population PK and explore relationships to study
covariates (Biomarkers, age, race, baseline disease severity, etc.)
Design: Structured for NONMEM software
STATISTICAL PROGRAMMING AND ANALYSIS
Considerations for ADaM & PK
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Current guidelines for CDISC ADaM limit PK data to the BDS structure
Works well for “Analysis-Ready” PK datasets supporting TLGs
Otherwise, determine best analysis format
Consider analysis tools FDA will use
Whatever analysis format to use, source to SDTM
Focus on “Fit for analysis”
CDISC Analysis Data Model Version 2.1:
The focus of any analysis format is to
Facilitate clear and unambiguous communication
Be readily useable by commonly available software tools
Provide traceability between the analysis data and its source data
(ultimately SDTM)
STATISTICAL PROGRAMMING AND ANALYSIS
Other Considerations
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Outsourcing
Four of the 7 studies with a PK data piece were outsourced
Create a plan for how to incorporate outsourced data into
your filing
Does outsourced SDTM / ADaM match Sponsor SDTM /
ADaM?
Do you have in-house all the pieces you need to create
analysis deliverables?
Can be difficult to assess if data is only used for
population PK analysis
STATISTICAL PROGRAMMING AND ANALYSIS
Questions
STATISTICAL PROGRAMMING AND ANALYSIS
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Additional Information
Pharmacology:
LABELING
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm065010.htm
BIOPHARMACEUTICS
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064964.htm
CLINICAL PHARMACOLOGY
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064982.htm
MODEL/DATA FORMAT
http://www.fda.gov/AboutFDA/CentersOffices/CDER/ucm180482.htm
SDTM / Define xml:
FDA (data standards)
http://www.fda.gov/ForIndustry/DataStandards/default.htm
CDISC (standards)
http://www.cdisc.org/standards
OpenCDISC (SDTM validator)
http://www.opencdisc.org/projects/validator
SAS (clinical standards toolkit)
http://support.sas.com/rnd/base/cdisc/cst/index.html
FDA Standards for Electronic Submissions:
SAS
http://www.sas.com/industry/government/fda/faq.html
STATISTICAL PROGRAMMING AND ANALYSIS
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Thank you to:
Bei Wang
Michael Ward
Laura Harris
Rick Graham
Jin Jin
Yongcun Zhang
Patty Gerend
STATISTICAL PROGRAMMING AND ANALYSIS
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